Category Archives: Python

No matter how handy graphical user interfaces are, the good old command line remains a useful tool for performing various low-level data manipulation and system administration tasks. It is the fallback when you need to do something that has no way of graphical control. Being much more expressive and open-ended than a predefined set of controls, the command shell is the ultimate control environment for your computer.

Data science has become one of the most intensely practised computer applications, so it is no wonder that it also benefits greatly from the hands-on control approach of the command line shell. Data scientist Jeroen Janssens has had the foresight to combine the fundamental operations of data science and the most suitable command line tools into a book that collects many useful practices, tips and tricks for processing and preparing data, called “Data Science at the Command Line” (O’Reilly, 2014).

At its highest abstraction levels, data science involves using models and machine learning to extract patterns from data and extrapolate results from data sets that are often much larger than fits in memory at any one time. At a lower level, it involves multiple file formats and just plain hard work to get the data in a fit shape to be analysed, and this is where the command line comes in.

There is only so much you can do with canned tools like text editors, but a world of possibilities opens for you when you have the power can chain simple commands together, forming pipelines of data where one command’s output becomes another one’s input. You can also redirect input from a file to a command, and from a command to a file.

Even though Linux and macOS installations have various command shells, apart from the defaults, Janssens shows you how to use a set of tools called the Data Science Toolbox, which actually uses VirtualBox or Vagrant to plant a self contained GNU/Linux environment with Python, R and various other tools of the trade on your local machine, without disturbing the host operating system too much.

With real-life examples, Janssens shows you how to use classic Linux command line tools like cut, grep, tr, uniq and sort to your advantage. You will also learn how to get data from the Internet, from databases and even Microsoft Excel spreadsheets, where most of the world’s operational data lies hidden from plain sight.

From this book I learned completely new and interesting ways to work with CSV (Comma Separated Value) files, and it introduced me to the excellent csvkit, with its collection of power tools to cut, merge and reorder columns in CSV files, perform SQL-style queries on the lines, and grep through them.

Among other things you get information on Drake, described as “make for data” – which, if you’re familiar with the classic software development tool make (and of course you are) should whet your appetite. There is also a chapter about how to make your data pipelines run faster by parallelising them and running commands on remote machines.

Scrubbing the data is less than half the fun, but usually more than half of the work in data science. You will learn to write executable scripts in Python and R with their comprehensive data science and statistics libraries, and learn to explore your data using visualisations that consist of statistical diagrams like bar charts and box plots. So the command line is not just text; even though the images are generated using commands, they are obviously shown in a GUI window.

Finally, there is a chapter on modelling data using both supervised and unsupervised learning methods, which serves as a cursory introduction to machine learning, although you are referred to more comprehensive texts on the algorithms involved.

At the back of the book there is a handy reference for all the commands discussed in the book, which include many of the old UNIX stalwarts found in Linux, but also newer tools like jq for processing JSON.

If you need to do data preparation for a data science project, you owe it to yourself to become good friends with the command line, and utilise the many tools described in Janssens’ book in your daily work. Even if you don’t “automate all the things“, you will benefit from the pipeline approach to data processing.

Back in 1998 or so, I wrote a CD player application for Microsoft Windows in Borland Delphi. It was for a magazine tutorial article, and I wanted a cool LCD-like display to show track elapsed and remaining time. There was a good one available for Delphi, called LCDLabel, written by Peter Czidlina (if you’re reading this, thanks once more for your cooperation).

I’ve been thinking about doing a modern version of the LCD display component for several times over the years, and I even got pretty far with one for OS X in 2010, but then abandoned it because of other projects. A few years ago I did some experiments with the LCD font file and wrote a small Python app to test it.

My most recent idea involving simulated LCD displays is to create a custom component for iOS and OS X in Swift. For that, I dug up the most recent Python project and tried to nail down the LCD font file format, so that I could later use it in Swift. I decided to use JSON.

The LCD font consists of character matrices, typically 5 columns by 7 rows, each describing a character on the LCD panel. The value of a matrix cell is one if the dot should be on, and zero if it should be off. I decided to store each cell value as an integer, even if it is a bit wasteful – but it is easy to maintain, and if you squint a bit, you can see the shape of the LCD character.

So the digit zero would be represented as a 2-dimensional matrix like this:

The font consists of as many characters as you like, but you need to identify them somehow. In JSON, you can do this with one-character strings, where the sole character is the Unicode code point of the character. So, with some additional useful information, a font with just the numeric digits 0, 1, and 2 would be represented in JSON like this:

Python is one of the friendliest general-purpose programming languages out there. It is free to use, well supported and used by many big companies. Since its introduction in 1991, it may not have taken the world by storm, but has gained a huge share of programmers’ interest. As of this writing (November 2014), Python is number 8 on the TIOBE Index.

Recently I have been studying bioinformatics, and in the course of my studies I have met many people who are learning to program for the first time, and doing it with Python. Others have a little bit of programming experience, but not in Python. Luckily Python is an excellent language for both groups, because it is clean and easy to learn, but it can still be powerful and expressive.

Beginners, step this way

Learning programming is not easy, but some of the things you need to understand are the same no matter what programming language you study. That is why I recommend Think Python by Allen Downey to all beginners. I’ve been programming for close to 30 years now, and I think that this book is one of the most accessible introductions to programming in general, and Python in particular. The subtitle of the book is “How to think like a computer scientist”, which essentially means “problem solving”. You need to be able to take apart what you are trying to achieve, and then find ways to make the computer do what you mean.

Think Python

Think Python is free to download from Green Tea Press in PDF format. However, if you want a printed book, you can buy one from O’Reilly.

Seasoned experts, check this out

I first learned Python in the early 2000s, when the language was still relatively unknown, but already had a lot of users. Since I learn best from a good book, I spent some time looking for one about Python, and quickly found Learning Python by Mark Lutz. At the time it was not a lean book anymore: the 2nd edition, which covers Python 2.3, already came up to almost 600 pages. Still, it is an easygoing book which has only gotten better with time.

Learning Python

In the recent years I’ve gone strictly e-book only, because I don’t have the shelf space for all the books I want or need, and e-books are also a lot cheaper. My whole programming library fits on my iPad, so it is with me wherever I go. New editions of a popular book like Learning Python typically accumulate more material over the years; the latest, 5th edition covers both Python 2.7 and 3.3, and comes up to (count ’em) 1540 pages. That might already be a little too much for a “learning” book, but there you have it.

To each their own

As a summary:

Absolute beginners in programming who want or need to learn Python, get Think Python by Allen Downey.

Those who already know a little bit about programming, and want to learn Python,
get Learning Python by Mark Lutz.

This post contains links to the O’Reilly webstore. If you follow the links and buy a book, I will get a minuscule commission. However, I was using both of these books professionally before I became an O’Reilly affiliate, and I want people to know about them and benefit from them.

Sometimes you just need to see what characters are lurking inside a Unicode encoded text file. Your garden variety dump utility (like the venerable od in UNIX systems and the Windows standard hex dump (though I don’t think there is one) only shows you the plain bytes, so you have to head over to unicode.org to find out what they mean. But first you need to decode UTF-8 to get the actual code points, or grok UTF-16 LE or BE, and so on. It’s fun, but it’s not for everyone.

The udump utility shows you a nice list of character names, together with their offsets in the file. Currently it only handles UTF-8, so the offset is calculated based on the UTF-8 length of the character.